Abstract
This chapter proposes a framework to make the programming of cobots faster, user-friendly and flexible for assembly tasks. The work focusses on an industrial case of a small (10kg) air compressor and investigates the technologies that can be used to automate this task with human-robot collaboration. To this end, the framework takes a radically different approach at the motion stack level and integrates the cobot with a constraint-based robot programming paradigm that enhances the robot programming possibilities. Additionally, the framework takes inputs from the operator via speech recognition and computer vision to increase the intuitiveness of the programing process. An implementation is made with focus on industrial robustness and the results show that this framework is a promising approach for the overall goal of achieving flexible assembly in the factories by making robot programming faster and intuitive.
TopIntroduction
Manufacturing is an activity that has been in flux since the industrial revolution. In its earliest days the focus was on minimizing energy losses and maximizing production numbers by focusing on the manufacturing techniques. A couple of centuries of innovation led to many breakthrough technologies like assembly lines, Programmable Logic Controllers, Manufacturing Execution Systems, Product Lifecycle Management etcetera. The modern world we inhabit is in many ways shaped by these innovations. It allows us to produce in huge numbers. However, this entire manufacturing process is very rigid. Rigid in the sense that the mass-produced items are al the same. They are generic in nature and try to appeal to a mass clientele. Any type of customization to suit the local tastes of a market is very difficult. Customization of products requires flexibility at the assembly lines. However, the entire manufacturing industry is composed of technologies, whose raison d'être is production in bulk. They are an antithesis to the very concept of customized production.
However, there is a clear market trend towards increasingly customized products. Manufacturing industry is changing at a breath-taking pace. Those companies that have given customization options on their products, instantly benefit with an increased market-share. It is in human nature to prefer a product that is customized for them. With the advent of many new regions of the world in manufacturing, the competition is very stiff and companies need to find ways to keep their market share. Customization of products is one such avenue that guarantees traction in the market. Therefore, we see a clear push from the companies to shift towards mass-customization. This trend gets the necessary push as technologies like robotics, artificial intelligence and faster processing speeds provide a theoretical avenue to make flexible assembly systems viable.
Figure 1. A flexible assembly work cell using a modern collaborative robot (cobot), that is safe for humans to work alongside, being taught by demonstration
Robotics, as a reconfigurable technology, can be leveraged to extend automation to unstructured tasks that can greatly facilitate such flexible production (Pan, 2012). For the manufacturing industry, this means a shift from mass production to mass customization, thus the ability to support small batch-size production. If a flexible assembly line is realized today with the current state-of-art in robotics, it would require frequent reprogramming of the robots every time the product is even slightly changed. This reprogramming comes at a high cost, both temporally and economically. Thus, the manufacturing process is not really flexible even though the hardware possesses the potential.
A typical factory requires the services of robotic integration companies to make any changes to their production process for a new product batch. The setup time for a typical application is around 3 months or greater (Jager, 2015). This poses a major hurdle towards more frequent reprogramming, especially for Small and Medium Enterprises (SME) to stay up to speed with the global demands. There is a strong interest from the industry to push down the cost of programming robots by making them fast and intuitive to program (Tirmizi, 2019). They envisage a future where integrators will have access to frameworks that allow quick programming of new robotic applications and any modifications required for a reprogramming will be done on the shop floor by interacting intuitively with the robots.
Key Terms in this Chapter
Human-Machine Interface: Also known as an HMI. An HMI is a software application that presents information to an operator or user about the state of a process, and to accept and implement the operators control instructions. Typically information is displayed in a graphic format (Graphical User Interface or GUI).
Programmable Logic Controllers: A programmable logic controller otherwise known as a PLC is an industrial computer control system that monitors the state of the input devices and makes decisions based upon a custom program to control the state of the output devices.
Manufacturing Execution Systems: A manufacturing execution system (MES) is an information system that connects, monitors and controls complex manufacturing systems and data flows on the factory floor. The main goal of an MES is to ensure effective execution of the manufacturing operations and improve production output.
Product Lifecycle Management: In industry, product lifecycle management (PLM) is the process of managing the entire lifecycle of a product from inception, through engineering design and manufacture, to service and disposal of manufactured products.
Original Equipment Manufacturer: An original equipment manufacturer (OEM) is a company that produces parts and equipment that may be marketed by another manufacturer.
Non-Sensitive Robot: A non-sensitive robot has no tactile sensing ability in its joint therefore it cannot determine if its body is in contact with anything external based on the interaction force.
Teach Pendent: Teach pendants are typically handheld devices and may be wired or wireless. A Teach Pendant is used to program various models of the robotic industrial machinery.
Collaborative Robots: A collaborative robot, also known as a cobot, is a robot that is capable of operating safely alongside human without any safety barrier separating the two. It has a complex sensor suite that not only allow safe operation but also makes it possible to assist humans in their tasks.
ROS: ROS is an open-source, meta-operating system for your robot. It provides the services you would expect from an operating system, including hardware abstraction, low-level device control, implementation of commonly-used functionality, message-passing between processes, and package management.
Docker: Docker is a tool that can package an application and its dependencies in a virtual container that can run on any Linux server. Docker containers are lightweight, a single server or virtual machine can run several containers simultaneously. It avoids the exercise to configure or reconfigure every time the application has to work on a new setup.
Robot End-Effector: In robotics, an end effector is the device at the end of a robotic arm, designed to interact with the environment. The exact nature of this device depends on the application of the robot.
ROS2: ROS2 is a more robust incarnation of ROS. It avoids a single point of failure. It is based on a more stable communication protocol. Its architecture offers the ability to do real-time control of robotic devices.